Silviase/jgov_v1.4_full_ann huggingface dataset:
access to information on foreign markets, the hypothesis goes, substantially reduces uncertainty and encourages firms to engage in export activities. Empirical work by Koenig et al. (2010) confirms this hypothesis by finding that the presence of other exporters has a positive effect on the export decision of other firms. Although Koenig et al. (2010) find evidence of positive export spillovers, the evidence produced by other empirical studies on such export spillovers is at best weak (e.g., Aitken et al. 1997, Barrios et al. 2003, Bernard and Jensen 2004), which means that the search for possible channels of information spillovers continues.

Against this background, this study focuses on information provided by lender banks as one potential channel of information spillovers. Most existing empirical studies examining information spillovers from other exporting firms assume that firms in the same region and/or industry are likely to exchange information with each other; however, such studies do not explicitly discuss the channel through which such information exchange takes place. The hypothesis we examine here is that lender banks work as a conduit for such information. In the case of Japan, lender banks provide not only financial support but also business consulting services utilizing extensive knowledge collected through their lending transaction relationships and from various information sources. Since the monitoring of borrower firms is important for banks, banks in general should accumulate information on borrower firms and related parties. Thus, if we assume that a particular bank is very knowledgeable about overseas business opportunities either through its own banking activities or transactions with client firms with experience in exporting, potential exporter firms would find it helpful to consult with such a bank. That financial institutions may indeed play an important role in determining client firms' export activities has recently been highlighted in studies by Amiti and Weinstein (2011) and Paravisini et al. (2011), which indicate that banks' financial health plays an important role in determining firms' export behavior. Inui et al. (2011), for example, focus on banks' ability to screen, monitor, and advise client firms as a determinant of export behavior. Specifically, using a measure of banks' efficiency as a proxy for their ability to screen, monitor, and advise client firms, they find that bank efficiency has a positive effect on the export decision and overseas sales ratio of client firms.

The aim of this paper is to explore the role of banks as information providers by explicitly quantifying banks' ability to provide information on export markets using a unique panel
dataset for Japan in which firms are matched to lender banks. $ ^{2} $ In fact, Japanese Bankers Association (2011) provides various examples of how banks provide supporting services to firms when the firms start exporting to a new foreign market and/or open affiliates or branches overseas. According to the report, banks not only provide financial support to firms but also actively introduce them to foreign firms that are potential business partners or providers of business supporting services. $ ^{3} $ We therefore conjecture that banks play a crucial role in substantially reducing the fixed entry costs incurred by client firms when starting to export. Specifically, we hypothesize that the provision of information by lender banks helps firms to start exporting based on the same mechanisms that information exchange with other exporting firms helps potential export starters. To examine this hypothesis, we focus on firms’ main bank which, in line with previous studies, we define as the top lender bank of a firm and investigate the importance of information flows from the main bank to client firms as a source of spillovers. $ ^{4} $

This paper contributes to the existing literature in at least two ways. First, it is the first study to examine the export decision by using a dataset that makes it possible to link firm-level information with information on the major lender banks of each firm. The paper explores the impact of information spillovers through main banks on both firms' decision to start exporting (the extensive margin) and on the volume exported by each firm (the intensive margin). Second, the paper investigates whether the importance of information provided by banks differs across export destination regions and examines what type of information – that is, general information on overseas markets regardless of the destination or destination-specific information – is more relevant for firms' export decision.

Our results show that information on overseas markets provided by a main bank substantially reduces the fixed costs of starting exporting for a firm and thereby increases the probability that the firm will start exporting. However, the effect of such information on the volume of exports is not very clear.

The organization of this paper is as follows. Section 2 briefly explains the roles that main banks play in Japan and presents the empirical strategy. Section 3 describes the dataset used
in this paper and provides some descriptive statistics on our sample firms. Next, Section 4 presents our estimation results. Finally, Section 5 discusses the policy implications and concludes.

### 2. Empirical Strategy

### 2.1 The Main Bank System in Japan

The “main bank system” has been a key feature of Japan’s economic system that can be traced back as far as the early post-war period. $ ^{5} $ In this system, a firm’s “main bank” usually is the bank from which it has borrowed the most and with which it typically has a long-term relationships. In addition, it is widely argued that main banks not only provide loans to client firms but also play a consulting role by providing relevant business information. In addition, main banks may get involved in the management of a firm in times of distress. Although the extent and form of main banks’ involvement in firms’ management in times of financial difficulties have been changing over time, main banks are still perceived to play an important role as providers of both funds and information to their client firms.

Trying to provide a theoretical underpinning for such long-term relationships between main banks and borrower firms, Patrick (1994) argues that such relationships enable banks to gain access to “soft information” on borrower firms, which helps to raise the efficiency of loan screening and borrower monitoring. The argument that repeated bank loan transactions lead to the accumulation of soft information on client firms has also been voiced in more recent studies such as Degryse et al. (2009).

Such soft information on borrower firms and banks’ own ability to collect information on industry-, region-, and nation-wide businesses has been helping Japanese main banks to provide effective and useful financial and consulting services to their client firms, and thereby has been contributing both to main banks’ profits and the growth of their client firms’ business. Particularly in recent years, aware of the fact that the growth prospects for Japan’s domestic market are not very promising and domestic manufacturing production has in fact been shrinking, banks have been promoting various services to support client firms’ international activities. With more and more Japanese large firms relocating production overseas, smaller domestic firms are forced to reduce their output, resulting in falling demand of funds, which in turn reduces business opportunities for banks in Japan. Moreover, if banks’ existing client firm went out of business, banks would not only lose current business but also future business in which to utilize the firm-specific soft information they have accumulated. Thus, faced with a potentially shrinking market at home, many banks in recent years have put greater emphasis on providing support.
services to client firms seeking to exploit growth opportunities overseas.

Concrete examples of the kind of support services that banks provide to their borrowers to help them with regard to international activities are provided by a Japanese Bankers Association (JBA) report (Japanese Bankers Association 2011). According to the report, other than traditional banking services such as the usual loan business, deposit services, payment services, lease and leaseback deals, or the issue of stand-by letters of credit, main banks often provide client firms with information on potential business partners in foreign countries as well as advice on recruiting employees, advertising, tax systems, and administrative issues such as accounting systems and laws and regulations. These examples indicate that banks provide not only financial transactions but also information services, and in the report, the JBA cites a survey it conducted according to which 38 out of 43 Japanese banks with activities in Asia say they provide services other than loan, deposit, and payment services. Specifically, 32 out of the 38 banks with activities in Asia say they provide information related to investment (i.e., tax and accounting systems, etc.), while 31 banks provide opportunities for business matching (e.g., organizing business matching events for Japanese firms and local partners). In addition, many banks provide information on firms located in destination regions (14 banks), loan guarantees (12 banks), and support with export and import procedure (8 banks).

### 2.2 Empirical Approach

This section explains the empirical strategy we employ to investigate the determinants of the export decision and of the export volume. We are particularly interested in the impact of information provided by main banks on the probability that a firm starts exporting (i.e., the extensive margin) and on the export volume (i.e., the intensive margin). Following previous empirical studies on the determinants of the extensive and intensive margin (e.g., Koenig et al. 2010, Minetti and Zhu 2011), we assume that firm i starts exporting if its profits are larger when exporting than when not exporting. Let $ \pi_{ijt}^{*} $ represent the difference between the profits of firm i when it starts exporting to destination j at time t and its profits when it does not start exporting to destination j at time t. The difference is determined by firm characteristics (e.g., size, productivity, and the skill level of workers), the firm's financial conditions (e.g., the leverage ratio, liquidity ratio, and short-term loan ratio), and the amount of information on the export market available to the firm. The availability of information on the export market is assumed to substantially lower the uncertainty of profits from exporting and hence, to lower either the variable or the fixed cost of exporting. While export spillovers are also taken into account, we are particularly interested in information provided through the main bank of the firm. Therefore, we parameterize $ \pi_{ijt}^{*} $ as:
$$ \pi_{\mathrm{i j t}}^{*}=\alpha_{1}+Z_{\mathrm{i t}}\beta_{1}+\mathrm{I}_{\mathrm{i j t}}\gamma_{1}+\varepsilon_{\mathrm{i j t}} $$

where $ Z_{it} $ is a vector of controls for firm characteristics and the firm's financial conditions which may affect firm i's differential profits $ \pi_{ijt}^{*} $ ; $ I_{ijt} $ is a vector of variables representing information available to the firm; and $ \varepsilon_{ijt} $ captures unobserved firm characteristics and other unknown factors that may also affect differential profits.

We assume that firm i starts exporting if the differential profits $ \pi_{ijt}^{*} > 0 $ . Under the assumption that $ \varepsilon_{ijt} $ is a normally distributed random error with zero mean and unit variance, the probability that firm i starts exporting can be written as:

$$ \mathrm{P r o b}_{\mathrm{i j t}}=\mathrm{P r o b}\big(\alpha_{1}+\mathrm{Z}_{\mathrm{i t}}\beta_{1}+\mathrm{I}_{\mathrm{i j t}}\gamma_{1}+\varepsilon_{\mathrm{i j t}}>0\big) $$

In the first instance, we estimate Eq. (1) with a random effect panel probit approach. In order to take any potential endogeneity into account, we lag all right-hand side variables by one year. $ ^{6} $ The dependent variable $ Prob_{ijt} $ denotes the change in export status at the firm- or firm-destination level and takes a value of 1 if a firm exports for the first time (overall) or the first time to destination j at time t. We define a firm as an export starter if the firm did not export over the last three years from t-3 to t-1 and exports at time t. $ Prob_{ijt} $ takes a value of 0 if a firm did not export to destination j for the last three years prior to year t and does not export in year t. Firms which always export to destination j are not included in our analysis. Regarding control variables for firm characteristics and the firm's financial conditions $ (Z_{it}) $ , we include firm size (the log of the number of employees of firm i), the TFP level of the firm, and the average wage rate of the firm as a proxy for the skill level of workers. Based on the results of both theoretical and empirical studies, we expect these variables to be positively correlated with firms' export decision. Further, to take the impact of liquidity constraints on firms' export behavior into account, we include variables representing firms' financial situation, such as their leverage ratio, their liquidity ratio, and the share of short-term loan in their total loans outstanding. The reason for including these variables is that, as highlighted by, e.g., Manova et al. (2011), Feenstra et al. (2011), and Minetti and Zhu (2011), financial constraints are likely to prevent firms from exporting because firms need sufficient liquidity in order to meet the entry costs associated with starting exporting. Therefore, we expect that firms with more liquidity are more likely to start exporting.

Regarding information available to the firm $ (I_{ijt}) $ , we include variables representing the
amount of information on export markets accumulated by a main bank and by a firm itself. The explanatory variable of main interest is the amount of information on export markets potentially available to the firm through its main bank, which is a proxy for the amount of information firm i's main bank has accumulated on destination j. Specifically, we measure this variable as the ratio of the number of the main bank's client firms that are exporting to destination j to the total number of the main bank's client firms, i.e., the intensity of each main bank's dealings with exporting firms. We conjecture that banks with more extensive dealings with exporter firms more effectively accumulate information related to overseas markets. This could be the case when, for example, banks allocate limited lending/managerial capacity to each lending activity. Under such circumstance, a higher intensity of dealing with exporting firms represents the extent to which the bank focuses (i.e., allocates internal resources with higher priority) on the lending activities accompanied by the provision of overseas market information. In addition, in order to take into account the information accumulated by firms themselves through their own international activities, we also include variables representing their overseas activities, such as the share of overseas employees in a firm's total number of employees and the share of overseas investment in a firm's total investment. $ ^{7} $ Industry dummies (for fifteen manufacturing industries) and time dummies are also included in order to control for industry-specific and time-specific fixed effects.

While Eq. (1) focuses on the extensive margin, i.e., whether firms start exporting, we also examine the role of information spillovers through the main bank on the intensive margin, i.e., the export volume after firms start exporting. To do so, we adapt Eq. (1) above as follows:

$$ \mathrm{EXP_{ijt}}=\alpha_{2}+\mathrm{Z_{it}}\beta_{2}+\mathrm{I_{ijt}}\gamma_{2}+\varepsilon_{\mathrm{ijt}} $$

where $ EXP_{ijt} $ is the log of firm i's exports to destination j at time t. We also use the first-difference of the log of exports (i.e., the growth rate of exports) as a dependent variable for an alternative specification. The variables on the right-hand side are the same as those in Eq. (1) and we again lag all variables by one year. As above, the variable we are most interested in is the amount of information on export markets potentially available to the firm through its main bank.

That the provision of information by the main bank may affect not only the extensive margin but also the intensive margin is suggested by the theoretical analysis by Rauch and Watson (2003), who examine the relationship between the search costs for establishing new partnerships
and export volumes. They suggest that the higher the costs of searching for a new supplier, the smaller tend to be the orders a buyer places with a supplier. In addition, buyers tend to place larger orders with suppliers once they know that the latter is able to fulfill larger orders. Based on this idea, if banks help in matching businesses in overseas markets and provide information to both the buyer and the supplier on their respective counterpart, this should substantially reduce uncertainty and possibly result in higher transaction volumes. We test this hypothesis by examining whether information spillovers through the main bank have a positive effect on the intensive margin or not. We should note that in the estimation of Eq. (2) non-exporters are excluded from the sample used for analysis. In cases such as here, where there is a risk of a selection bias, a typical solution employed often is to use a Heckman selection model. However, we do not employ the Heckman model and estimate Eq.s (1) and (2) separately, since it is difficult to find a variable which strongly affects the selection process (Eq. (1)) but not the outcome (Eq. (2)). $ ^{8} $ Therefore, we estimate Eq. (2) separately from Eq. (1), employing the fixed-effect panel estimation method.

### 3. Data and Descriptive Statistics

### 3.1 Data Description

The data used in this study are the firm-level panel data from the Basic Survey on Business Structure and Activities (BSBSA) collected annually by Ministry of Economy, Trade and Industry (METI) for the period 1997-2008. The survey is compulsory and covers all firms with at least 50 employees or 30 million yen of paid-in capital in the Japanese manufacturing, mining, and wholesale and retail sectors and several other service sectors. The survey contains detailed information on firm-level business activities such as the 3-digit industry in which the firm operates, its number of employees, sales, purchases, exports, and imports (including a breakdown of the destination of sales and exports and the origin of purchases and imports). $ ^{9,10} $ It also contains R&D expenditures and patents owned, the number of domestic and overseas subsidiaries, and various other financial data such as costs, profits, investment, debt and assets.

The key aim of our analysis, as mentioned above, is to investigate the importance of
information on destination markets and advice provided by main banks to their client firms. To do so, we combine the firm-level data with information on firms' main bank and examine the relationships between firm characteristics, main banks' ability to provide advice, and firms' export status. We augment the firm-level panel data taken from the BSBS A with information on firm characteristics stored in the Development Bank of Japan Corporate Financial Databank. We then merge the dataset with information on the main bank for each firm using the loan relation information stored in the NEEDS Financial Quest database. This database also includes various types of information on main banks.

Although the BSBSA includes a large number of unlisted firms, we have to restrict our sample to listed firms because the information on firms' bank loan relationships is available for listed firms only. Yet, even though we limit our sample to listed companies so that we can match firms to their main bank, our dataset nevertheless includes a considerable number of relatively small firms, which are listed on the stock exchange markets for start-up companies, and some of them are first-time exporters. Moreover, once firms have started exporting, many of them expand the range of destinations to which they export, so that when we examine the determinants of whether firms start exporting to a new destination, we can include more observations in our analysis.

Our unbalanced panel data contain approximately 300–400 listed firms per year, approximately 5 percent of which are identified as export starters. $ ^{[11]} $ Although the number of pure first-time exporters is limited, there are a substantial number of exporters that expanded or reduced the number of destinations to which they exported during our observation period.

### 3.2 Variables

Let us now describe the variables for our estimation in detail. Basic statistics of all variables are provided in Table 1. Starting with the dependent variable, to estimate the extensive margin we construct three kinds of dummy variables. The first of these is $ NEW\_EXP $ , which takes a value of 1 if the firm did not export to any of the regions considered in our analysis (i.e., Asia, North America, Central and South America, Africa, and Oceania) in years t-3 to t-1 but exported in year t. $ ^{12} $ The aim of using this three-year window is to identify export starters as unambiguously
as possible. While employing this definition means that export starters still include firms that have past export experience and therefore are not pure first-time exporters, using a three-year window should reduce any possible biases arising from the misidentification of new exporters. $ ^{13} $ The second, alternative dependent variable we use is $ NEW\_EXP\_REGION $ , which takes a value of 1 if the firm did not export to one of the regions we focus on (i.e., Asia, North America, Central and South America, Africa, and Oceania) in years t-3 to t-1 but did export to one of those regions in year t. The third dependent variable is defined by region. Thus, $ NEW\_EXP\_ASIA $ takes a value of 1 if the firm did not export to Asia in years t-3 to t-1 but did export to Asia in year t. In the same manner, we define $ NEW\_EXP\_NA $ , $ NEW\_EXP\_CSA $ , $ NEW\_EXP\_AFR $ , and $ NEW\_EXP\_OCE $ , for the decision to export to North America, Central and South America, Africa, and Oceania, respectively.

Let us next look at our explanatory variables. The variable we are particularly interested in is the variable measuring the potential information spillovers through a main bank, BANKINFO. In order to construct the BANKINFO variable, we first construct the variable NUM_EXPORTER, which denotes the number of each bank’s exporting client firms. We should note that for the NUM_EXPORTER variable, exporting firms for which a bank is not the main bank (i.e., not the top lender) are included. In this sense, we implicitly assume that all loan exposures to firms potentially contribute to the accumulation of overseas information at banks. $ ^{14} $ Therefore, the NUM_EXPORTER variable measures how many firms that could serve as a source of overseas information a firm’s main bank transacts with. Given that NUM_EXPORTER is highly correlated with banks’ size, we define BANKINFO as the ratio of NUM_EXPORTER to the total number of the bank’s client firms (NUM_CLIENT). Through this metric, we intend to measure the intensity of each bank’s exposure to exporting firms. $ ^{15} $ Since we have information regarding which regions